/*
 *   This program is free software: you can redistribute it and/or modify
 *   it under the terms of the GNU General Public License as published by
 *   the Free Software Foundation, either version 3 of the License, or
 *   (at your option) any later version.
 *
 *   This program is distributed in the hope that it will be useful,
 *   but WITHOUT ANY WARRANTY; without even the implied warranty of
 *   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *   GNU General Public License for more details.
 *
 *   You should have received a copy of the GNU General Public License
 *   along with this program.  If not, see <http://www.gnu.org/licenses/>.
 */

/*
 *    IteratedSingleClassifierEnhancer.java
 *    Copyright (C) 2004-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers;

import java.util.Collections;
import java.util.Enumeration;
import java.util.Vector;

import weka.core.Instances;
import weka.core.Option;
import weka.core.Utils;

/**
 * Abstract utility class for handling settings common to meta classifiers that
 * build an ensemble from a single base learner.
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision$
 */
public abstract class IteratedSingleClassifierEnhancer extends SingleClassifierEnhancer {

    /** for serialization */
    private static final long serialVersionUID = -6217979135443319724L;

    /** Array for storing the generated base classifiers. */
    protected Classifier[] m_Classifiers;

    /** The number of iterations. */
    protected int m_NumIterations = defaultNumberOfIterations();

    /**
     * The default number of iterations to perform.
     */
    protected int defaultNumberOfIterations() {
        return 10;
    }

    /**
     * Stump method for building the classifiers.
     *
     * @param data the training data to be used for generating the bagged
     *             classifier.
     * @exception Exception if the classifier could not be built successfully
     */
    public void buildClassifier(Instances data) throws Exception {

        if (m_Classifier == null) {
            throw new Exception("A base classifier has not been specified!");
        }
        m_Classifiers = AbstractClassifier.makeCopies(m_Classifier, m_NumIterations);
    }

    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration<Option> listOptions() {

        Vector<Option> newVector = new Vector<Option>(2);

        newVector.addElement(new Option("\tNumber of iterations.\n" + "\t(current value " + getNumIterations() + ")", "I", 1, "-I <num>"));

        newVector.addAll(Collections.list(super.listOptions()));

        return newVector.elements();
    }

    /**
     * Parses a given list of options. Valid options are:
     * <p>
     *
     * -W classname <br>
     * Specify the full class name of the base learner.
     * <p>
     *
     * -I num <br>
     * Set the number of iterations (default 10).
     * <p>
     *
     * Options after -- are passed to the designated classifier.
     * <p>
     *
     * @param options the list of options as an array of strings
     * @exception Exception if an option is not supported
     */
    public void setOptions(String[] options) throws Exception {

        String iterations = Utils.getOption('I', options);
        if (iterations.length() != 0) {
            setNumIterations(Integer.parseInt(iterations));
        } else {
            setNumIterations(defaultNumberOfIterations());
        }

        super.setOptions(options);
    }

    /**
     * Gets the current settings of the classifier.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {

        String[] superOptions = super.getOptions();
        String[] options = new String[superOptions.length + 2];

        int current = 0;
        options[current++] = "-I";
        options[current++] = "" + getNumIterations();

        System.arraycopy(superOptions, 0, options, current, superOptions.length);

        return options;
    }

    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for displaying in the
     *         explorer/experimenter gui
     */
    public String numIterationsTipText() {
        return "The number of iterations to be performed.";
    }

    /**
     * Sets the number of bagging iterations
     */
    public void setNumIterations(int numIterations) {

        m_NumIterations = numIterations;
    }

    /**
     * Gets the number of bagging iterations
     *
     * @return the maximum number of bagging iterations
     */
    public int getNumIterations() {

        return m_NumIterations;
    }
}
